article · Computational Intelligence and Neuroscience
Multipose face recognition remains a technical challenge in security applications. Existing efforts have focused on refining detectors such as Viola-Jones and Real Adaboost, or improving recognition models including support vector machines and deep convolutional neural networks. A combined adaptive deep learning vector quantization classifier addresses known limitations of standard adaptive vector quantization techniques by incorporating a majority voting algorithm alongside a speeded up robust feature extractor. Experimental assessments indicate that this combined structure delivers promising outcomes across several performance criteria, specifically sensitivity, specificity, precision, and accuracy. When compared against modern statistical approaches, classical neural networks, and deep learning architectures, empirical evaluation with confusion matrices demonstrates the reliability and robustness of the approach relative to existing state of the art systems.
Identifying individuals from varied facial angles is essential for practical biometric security and automated monitoring. Improving classification precision and sensitivity helps overcome identification failures when subjects do not directly face cameras, thereby enhancing the overall dependability of computer vision systems deployed in surveillance and identity verification environments.
This research is applicable to security monitoring and automated biometric verification software where subjects appear in arbitrary poses. Intended users include developers of access control, public security, and identity authentication platforms. As the abstract describes experimental and algorithmic comparisons against other machine learning models, the technology represents early-stage research that requires development into full software pipelines before commercial deployment.
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Multipose face recognition system is one of the recent challenges faced by the researchers interested in security applications. Different researches have been introduced discussing the accuracy improvement of multipose face recognition through enhancing the face detector as Viola-Jones, Real Adaboost, and Cascade Object Detector while others concentrated on the recognition systems as support vector machine and deep convolution neural networks. In this paper, a combined adaptive deep learning vector quantization (CADLVQ) classifier is proposed. The proposed classifier has boosted the weakness of the adaptive deep learning vector quantization classifiers through using the majority voting algorithm with the speeded up robust feature extractor. Experimental results indicate that, the proposed classifier provided promising results in terms of sensitivity, specificity, precision, and accuracy compared to recent approaches in deep learning, statistical, and classical neural networks. Finally, the comparison is empirically performed using confusion matrix to ensure the reliability and robustness of the proposed system compared to the state-of art.
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DOI: 10.1155/2020/8821868
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